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Proceedings Paper

Medical image segmentation using level set and watershed transform
Author(s): Fuping Zhu; Jie Tian
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Paper Abstract

One of the most popular level set algorithms is the so-called fast marching method. In this paper, a medical image segmentation algorithm is proposed based on the combination of fast marching method and watershed transformation. First, the original image is smoothed using nonlinear diffusion filter, then the smoothed image is over-segmented by the watershed algorithm. Last, the image is segmented automatically using the modified fast marching method. Due to introducing over-segmentation, the arrival time the seeded point to the boundary of region should be calculated. For other pixels inside the region of the seeded point, the arrival time is not calculated because of the region homogeneity. So the algorithm’s speed improves greatly. Moreover, the speed function is redefined based on the statistical similarity degree of the nearby regions. We also extend our algorithm to 3D circumstance and segment medical image series. Experiments show that the algorithm can fast and accurately obtain segmentation results of medical images.

Paper Details

Date Published: 22 July 2003
PDF: 9 pages
Proc. SPIE 4958, Advanced Biomedical and Clinical Diagnostic Systems, (22 July 2003); doi: 10.1117/12.488685
Show Author Affiliations
Fuping Zhu, Institute of Automation, CAS (China)
Jie Tian, Institute of Automation, CAS (China)

Published in SPIE Proceedings Vol. 4958:
Advanced Biomedical and Clinical Diagnostic Systems
Tuan Vo-Dinh; Warren S. Grundfest M.D.; David A. Benaron M.D.; Gerald E. Cohn, Editor(s)

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